GANs can do a lot more than generating sine signals. We can apply GANs to address many different practical problems by altering the input and output dimensions of the generator and combining them with other methods. For example, we can generate text and audio (1-dimension), images (2-dimension), video, and 3D models (3-dimension) based on random input. If we keep the same dimension of input and output, we can perform denoising and translation on these types of data. We can feed real data into the generator and let it output data with larger dimensions, for example, image super-resolution. We can also feed one type of data and let it give another type of data, for example, generate audio based on text, generate images based on text, and so on.
Even though it has only been 4 years since GANs first came out (at the time of writing), people have kept working on improving...
